ECCS-EPSRC: NeuroComm: Brain-Inspired Wireless Communications -- From Theoretical Foundations to Implementation for 6G and Beyond
Lead Research Organisation:
King's College London
Department Name: Engineering
Abstract
Current wireless systems, from Wi-Fi to 5G, have been designed by following principles that have not changed over the last 70 years. This approach has given us dependable, universal wireless connectivity solutions that can deliver any type of digital information. As computing systems substitute universal digital processors with specialised circuits for artificial intelligence (AI), and as wireless connectivity becomes an integral part of the sensing-compute-actuation fabric powered by AI, it is essential to rethink the fundamental principles underpinning the design of wireless systems. The global telecom market is estimated at around USD 850 billion, with the UK telecom industry generating around GBP 30 billion in 2020. The countries that will lead in the creation of the new technological principles and capabilities underpinning 6G will have a significant international market edge, making fundamental research on the subject a critical national policy issue.
In this context, neuromorphic sensing and computing are emerging as alternative, brain-inspired, paradigms for efficient data collection and semantic signal processing that build on event-driven measurements, in-memory computing, spike-based information processing, reduced precision and increased stochasticity, and adaptability via learning in hardware. The neuromorphic sensing and computing market was valued at USD 22.5 million in 2020, and it is projected to be worth USD 333.6 million by 2026. Current commercial use cases of neuromorphic technologies range from drone monitoring to the development of fast and accurate COVID-19 antibody testing. NeuroComm views the emergence of neuromorphic technologies as a unique opportunity for the development of efficient, integrated wireless connectivity and semantic processing -- referred to broadly as wireless cognition. Specifically, NeuroComm aims systematically addressing the integration of neuromorphic principles within an end-to-end system encompassing sensing, computing, and wireless communications.
The informational currency of neuromorphic computing is not the bit, but the timing of spikes. Neuroscientists have long studied the efficiency and effectiveness of spike-based communications in biological neurons. In the context of wireless cognition, spike-based processing and communication raise novel fundamental questions regarding optimal joint signaling and computing strategies. NeuroComm will take the approach of starting from first, information-theoretic, principles, addressing the problem of what to implement before investigating how to best deploy neuromorphic based wireless cognition. To this end, the project aims at developing an information-theoretic framework for the analysis of wireless cognition systems with neuromorphic transceivers. The efficiency of neuromorphic computing hinges on the co-design of hardware and software. NeuroComm posits that a close integration of neuromorphic computing and communications at the design stage will be needed in order to fully leverage the benefits of brain-inspired wireless cognition.
NeuroComm is a collaboration between King's College London (KCL) as lead institution and Princeton University (PU) as academic partner, along with NVIDA, Intel Labs, AccelerComm, and IBM Zurich as industrial partners. The research will build on the PIs' expertise in information theory, machine learning, communications, and neuromorphic computing to explore theoretical foundations, algorithms, and hardware implementation.
In this context, neuromorphic sensing and computing are emerging as alternative, brain-inspired, paradigms for efficient data collection and semantic signal processing that build on event-driven measurements, in-memory computing, spike-based information processing, reduced precision and increased stochasticity, and adaptability via learning in hardware. The neuromorphic sensing and computing market was valued at USD 22.5 million in 2020, and it is projected to be worth USD 333.6 million by 2026. Current commercial use cases of neuromorphic technologies range from drone monitoring to the development of fast and accurate COVID-19 antibody testing. NeuroComm views the emergence of neuromorphic technologies as a unique opportunity for the development of efficient, integrated wireless connectivity and semantic processing -- referred to broadly as wireless cognition. Specifically, NeuroComm aims systematically addressing the integration of neuromorphic principles within an end-to-end system encompassing sensing, computing, and wireless communications.
The informational currency of neuromorphic computing is not the bit, but the timing of spikes. Neuroscientists have long studied the efficiency and effectiveness of spike-based communications in biological neurons. In the context of wireless cognition, spike-based processing and communication raise novel fundamental questions regarding optimal joint signaling and computing strategies. NeuroComm will take the approach of starting from first, information-theoretic, principles, addressing the problem of what to implement before investigating how to best deploy neuromorphic based wireless cognition. To this end, the project aims at developing an information-theoretic framework for the analysis of wireless cognition systems with neuromorphic transceivers. The efficiency of neuromorphic computing hinges on the co-design of hardware and software. NeuroComm posits that a close integration of neuromorphic computing and communications at the design stage will be needed in order to fully leverage the benefits of brain-inspired wireless cognition.
NeuroComm is a collaboration between King's College London (KCL) as lead institution and Princeton University (PU) as academic partner, along with NVIDA, Intel Labs, AccelerComm, and IBM Zurich as industrial partners. The research will build on the PIs' expertise in information theory, machine learning, communications, and neuromorphic computing to explore theoretical foundations, algorithms, and hardware implementation.
